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CNNs with large kernels attract some research attention recently. Except for the global CNN learning in the Fourier domain [1]}, Ding et al. [2]} propose to enlarge the kernel size to 31\(\times \) 31 with several tricks and achieve better performance in many downstream tasks. The kernel size is further enlarged to 51 with the sparsity in [3]}. To balance the computation, large kernel convolutions are decomposed into a depth-wise convolution, a depth-wise dilation convolution, and a point-wise convolution in [4]}.
We show that such approximately large kernels can work properly in the image inpainting compared with vanilla convolutions, dilated ones, and even FFC.
